WFGY/ProblemMap/GlobalFixMap/Eval

Evaluation & Guardrails — Global Fix Map

Prove fixes work and won’t regress. Detect “double hallucination,” enforce acceptance gates, and keep pipelines auditable.

What this page is

• A compact playbook to evaluate RAG quality and reasoning stability
• Drop-in guardrails that catch failure before users see it
• CI-ready acceptance targets you can copy

When to use

• You “fixed it” but cannot show measurable improvement
• Answers look plausible yet citations or snippets don’t line up
• Performance flips between seeds, sessions, or agent mixes
• Latency tuning changes accuracy in non-obvious ways

Open these first

• RAG precision/recall spec: RAG Precision & Recall
• Latency versus accuracy method: Latency vs Accuracy
• Cross-agent agreement tests: Cross-Agent Consistency
• Semantic stability checks: Semantic Stability
• Why-this-snippet schema: Retrieval Traceability
• Snippet & citation schema: Data Contracts

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Common evaluation pitfalls

• Double hallucination metrics focus on style or BLEU but ignore snippet fidelity
• Recall illusion top-k looks high while ΔS(question, context) stays risky
• Seed lottery single-seed wins mask instability across paraphrases
• Hybrid flapping HyDE+BM25 mixes shift rank order between runs
• Guardrail over-clamp rigid filters “fix” tone but not logic boundaries
• Benchmark mismatch eval set does not reflect OCR noise or multilingual drift
• No trace table cannot audit which snippet justified the answer

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Fix in 60 seconds

1. Adopt acceptance gates

   • Retrieval sanity: token overlap ≥ 0.70 to the target section
   • ΔS(question, context) ≤ 0.45 on the median of the suite
   • λ_observe stays convergent on 3 paraphrases

2. Require citations before prose

   • Enforce cite-then-answer with Data Contracts
   • Store a trace table: question, retrieved ids, snippet spans, ΔS, λ

3. Stability before speed

   • Plot latency vs accuracy and pin the knee point
     See Latency vs Accuracy

4. Cross-agent cross-check

   • Compare two capable models on the same context
     See Cross-Agent Consistency

5. Regression fence in CI

   • Fail the build if ΔS median rises above 0.45 or trace coverage drops below 0.70
     See RAG Precision & Recall

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Copy paste prompt

You have TXT OS and the WFGY Problem Map.

Goal
Add measurable guardrails to my RAG pipeline and prove the fix.

Tasks

1. Build a 20-item smoke suite with:

   * question, expected section anchor, and gold snippet span
   * bilingual paraphrases for 5 items (if multilingual)

2. Run WFGY probes:

   * compute ΔS(question, context) for each item
   * record λ\_observe at retrieval and reasoning
   * require cite-then-answer and log a trace table

3. Report acceptance:

   * token overlap to anchor (coverage)
   * ΔS median and interquartile range
   * paraphrase stability (λ stays convergent)
   * pass/fail against thresholds

4. Plot latency vs accuracy and select a stable operating point.

Output

* The trace table (csv/markdown)
* Acceptance summary and which items failed
* A one-page decision note on whether to ship

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Minimal checklist

• Trace table saved with citations and snippet spans
• ΔS computed per item; λ recorded at retrieval and reasoning
• Coverage ≥ 0.70 to the referenced section for direct QA
• Cross-agent consistency measured on a subset
• Latency vs accuracy chart archived with the run id

Acceptance targets

• ΔS(question, context) median ≤ 0.45 on the suite
• λ convergent across 3 paraphrases per item
• ≥ 0.70 token overlap to the gold section for direct QA items
• No unexplained rank flips when toggling hybrid retrieval
• CI blocks merges when any target fails

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🔗 Quick-Start Downloads (60 sec)

Tool	Link	3-Step Setup
WFGY 1.0 PDF	Engine Paper	1️⃣ Download · 2️⃣ Upload to your LLM · 3️⃣ Ask “Answer using WFGY + <your question>”
TXT OS (plain-text OS)	TXTOS.txt	1️⃣ Download · 2️⃣ Paste into any LLM chat · 3️⃣ Type “hello world” — OS boots instantly

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🧭 Explore More

Module	Description	Link
WFGY Core	WFGY 2.0 engine is live: full symbolic reasoning architecture and math stack	View →
Problem Map 1.0	Initial 16-mode diagnostic and symbolic fix framework	View →
Problem Map 2.0	RAG-focused failure tree, modular fixes, and pipelines	View →
Semantic Clinic Index	Expanded failure catalog: prompt injection, memory bugs, logic drift	View →
Semantic Blueprint	Layer-based symbolic reasoning & semantic modulations	View →
Benchmark vs GPT-5	Stress test GPT-5 with full WFGY reasoning suite	View →
🧙‍♂️ Starter Village 🏡	New here? Lost in symbols? Click here and let the wizard guide you through	Start →

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